Mallette et al. AMB Express 2012, 2:23
http://www.amb-express.com/content/2/1/23
ORIGINAL ARTICLE
Open Access
Resolution of volatile fuel compound profiles
from Ascocoryne sarcoides: a comparison by
proton transfer reaction-mass spectrometry and
solid phase microextraction gas chromatographymass spectrometry
Natasha D Mallette1,2, W Berk Knighton3, Gary A Strobel4, Ross P Carlson1,2 and Brent M Peyton1,2*
Abstract
Volatile hydrocarbon production by Ascocoryne sacroides was studied over its growth cycle. Gas-phase compounds
were measured continuously with a proton transfer reaction-mass spectrometry (PTR-MS) and at distinct time
points with gas chromatography-mass spectrometry (GC-MS) using head space solid phase microextraction (SPME).
The PTR-MS ion signal permitted temporal resolution of the volatile production while the SPME results revealed
distinct compound identities. The quantitative PTR-MS results showed the volatile production was dominated by
ethanol and acetaldehyde, while the concentration of the remainder of volatiles consistently reached 2,000 ppbv.
The measurement of alcohols from the fungal culture by the two techniques correlated well. Notable compounds
of fuel interest included nonanal, 1-octen-3-ol, 1-butanol, 3-methyl- and benzaldehyde. Abiotic comparison of the
two techniques demonstrated SPME fiber bias toward higher molecular weight compounds, making quantitative
efforts with SPME impractical. Together, PTR-MS and SPME GC-MS were shown as valuable tools for characterizing
volatile fuel compound production from microbiological sources.
Keywords: Biofuel, Solid phase microextraction, Proton transfer reaction-mass spectrometry, Volatile organic compounds, Fungal hydrocarbons, Gas chromatography-mass spectrometry
Introduction
Ascocoryne sarcoides is an endophytic fungus recently isolated from Northern Patagonia and has the potential to
produce a petroleum-like fuel (Mycodiesel®) directly from
a cellulose fermentation process [Strobel, Knighton, Kluck,
Ren, Livinghouse, Griffin, Spakowicz and Sears 2008].
Fungi produce medium to long chain hydrocarbons, and
the prevalence of these compounds is in the C19-C30 chain
length [Ladygina, Dedyukhina and Vainshtein 2006]. In
contrast, Ascocoryne sarcoides (NRRL 50072) has been
reported to produce a series of straight chained and
branched medium chain-length hydrocarbons of C5-C10
chain length, in the range of gasoline fuel, including
* Correspondence: BPeyton@coe.montana.edu
1
Department of Chemical and Biological Engineering, Montana State
University, Bozeman MT 59717, USA
Full list of author information is available at the end of the article
heptane, 2-pentene, octane, 1-methyl-cyclohexene, 3,5octadiene, and cyclodecene [Solomons and Fryhle 2002;
Griffin, Spakowicz, Gianoulis and Strobel 2010]. This
interesting metabolism requires further work to characterize growth patterns and develop the organism for potential
biofuel applications.
Recent studies have identified a variety of compounds
produced by A. sarcoides on different media using headspace GC-MS on discrete solid phase microextraction
(SPME) fiber samples [Griffin, Spakowicz, Gianoulis and
Strobel 2010; Strobel et al. 2008]. The SPME technique
was introduced in the 1990’s [Arthur and Pawliszyn 1990],
and SPME fibers have been used to identify and quantify
complex volatile mixtures including hydrocarbons from
water, soil, food and wine [Langenfeld et al. 1996; Mallouchos et al. 2002; Parkerton et al. 2000; Robinson et al.
2011]. Its advantages include minimizing systematic errors
© 2012 Mallette et al; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
Mallette et al. AMB Express 2012, 2:23
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with extraction of volatiles, eliminating the need for solvents [Wercinski 1999], and being simple, fast and relatively sensitive [Pawliszyn 1999]. However, several studies
have revealed instabilities with quantification by this
method [Brás et al. 2011; Weldegergis et al. 2011; Zeng
and Noblet 2002], so further evaluation of the quantitative
use of the method for volatile mixtures from A. sarcoides
was tested.
Another analytical method with potential for analyzing
biofuels, proton transfer reaction-mass spectrometry
(PTR-MS) has been used to monitor volatile organic
compounds (VOCs) emitted from plants, bacteria and
subterranean fungi [Aprea et al. 2007; 2009, Bäck et al.
2010; Bunge et al. 2008; Kai et al. 2010; Maleknia et al.
2007; Ramirez et al. 2009; Steeghs 2004]. These studies
often employed solid substrata, rarely included submerged liquid fungal cultivation [Bäck et al. 2010], and
collected data in discrete time points. To our knowledge,
however, no studies have used PTR-MS to investigate the
continuous evolution of volatile organic fuel compounds
from a submerged liquid fungal culture over its entire
growth cycle.
An advantage of PTR-MS is that it can be run in real
time to continuously collect data on the concentrations
of specific compounds [Ezra et al. 2004; Lindinger et al.
1998; Tomsheck et al. 2010]. PTR-MS is primarily a
quantitative tool and generally requires a detailed
knowledge of the components present to be successfully
employed. Ion mass (as measured by a quadrupole mass
spectrometer) is not a unique indicator of chemical
composition. For instance, an ion at m/z 107 can represent either C8H10H+ (ethylbenzene or xylene isomer) or
C7H6OH+ (benzaldehyde). Beyond the identification of a
few small molecules, such as acetaldehyde and methanol, interpretation of the PTR-MS measurements of
complex VOC mixtures benefits from the support of
another separation technique to provide identification of
components.
SPME GC-MS has been combined with PTR-MS to
better elucidate VOCs from plants [Aprea et al.
2009Bouvier-Brown et al. 2007; Ruth et al. 2005] and
human lungs [Bajtarevic et al. 2009; King et al. 2010].
These studies showed the combined techniques allowed
for both qualitative and quantitative determinations of
VOCs. The use of SPME in conjunction with PTR-MS
added qualitative value to the interpretation of the PTRMS data and strengthened the information obtained
from experimentation, particularly as it applied to biological metabolic processes. The knowledge of compounds
present obtained by SPME allowed elucidation of the
most probable sources assigned to ions measured by the
PTR-MS.
Here, PTR-MS was combined with headspace SPME
GC-MS to explore the applicability of both methods for
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time-course resolution and quantification of volatiles
emitted over the culture cycle of A. sarcoides. We present the results of the continuous PTR-MS method as
compared with those of discrete headspace SPME GCMS for monitoring the activity of a novel fungal culture
and show both the benefits and shortcomings of each
technique.
Materials and methods
Culture medium and conditions
Ascocoryne sarcoides (NRRL 50072) was provided by Dr.
Gary Strobel and grown in a Biostat B Twin Plus 5-liter
jacketed reactor (Sartorius Stedim Biotech) on a minimal
medium consisting of (per liter): glucose (20 g), ammonium chloride (5 g), NaH 2 PO 4 . 2H 2 O (2.75 g),
MgSO 4 . 7H 2 O (0.86 g), Ca(NO 3 ) 2 . 4H 2 O (.28 g), yeast
extract (0.05 g), and trace salts: KCl (60 mg), KNO3 (80
mg), FeCl3 (2 mg), MnCl2 (5 mg), ZnSO4 (2.5 mg), H3BO3
(1.4 mg), and KI (0.7 mg). The minimal medium was used,
in contrast to a more nutrient rich one, to minimize the
amount of volatile compounds originating from the medium. An un-inoculated control reactor was run to account
for background VOCs resulting from the medium constituents or air supply. Both vessels were sparged with air
from compressed air cylinders at 1.5 L/min. This sparge
air was humidified prior to introduction into the Biostat to
minimize evaporative losses. The pH was controlled at
4.5 with 2 M HCl, 2 M NaOH and was continuously monitored by an internal Hamiltion Easyferm Plus probe. In
addition, the stir rate (250 rpm) and temperature (23°C)
were held constant by the Biostat control system. The
reactor gaseous effluent was sent to the PTR-MS and an
off-gas analyzer for O2 and CO2 measurements.
Accumulation of A. sarcoides was analyzed through daily
samples of cell dry weight, where samples were filtered
onto glass fiber filters (Whatman GF/F), rinsed, and dried
overnight in an oven at 80°C. Once removed from the
oven, filters were allowed to equilibrate at room temperature in a desiccator and then weighed. Daily glucose concentrations were measured by High Performance Liquid
Chromatography (HPLC), Agilent 1200 using an Aminex
HPX-87H ion exclusion column at 45°C with 0.005 M
H2SO4 eluent.
Quantification of volatiles by PTR-MS
Proton transfer reaction-mass spectrometry (PTR-MS)
was used to quantify a selected set of volatile compounds
produced by A. sarcoides by constantly monitoring the
gaseous effluent from the cultured reactor into the stationary phase of growth. The PTR-MS instrument ionized
organic molecules in the gas phase through their reaction
with H3O+, forming protonated molecules (MH+, where
M is the neutral organic molecule) and fragment ions,
which were detected by a standard quadrupole mass
Mallette et al. AMB Express 2012, 2:23
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spectrometer. This process can be used with volatiles in
air with or without dilution, since the primary constituents
of air (nitrogen, oxygen, argon and carbon dioxide) have a
proton affinity less than water and thus are not ionized.
Most organic molecules (excepting alkanes) have a proton
affinity greater than water and are therefore ionized and
detected [Lindinger et al. 1998].
The cultured and un-inoculated controls were analyzed
by diverting a portion of the reactor effluent gas to the
PTR-MS. Measurements were made from the initial
inoculation until 18.5 days. At times, the gas stream was
diluted with additional air to prevent premature degradation of the MS detector and keep measurements within
the linear dynamic range of the PTR-MS. During dilution
periods, the dilution stream was added to the reactor effluent stream and balanced with the effluent flow rate using
flow controllers. Sample lines were constructed from PFA
Teflon tubing and stainless steel fittings. Mass spectral
scans were acquired from 20 to 220 amu at 0.5 sec/amu.
The absolute concentration of constituents in a sample
were quantified directly from the measured ion intensities
using the known reaction time and the theoretical reaction
rate constant for the proton transfer reaction [Lindinger et
al. 1998]. Concentrations reported within were derived
from the PTR-MS measurements and calculated using
equations derived from reaction kinetics, assuming a reaction rate coefficient of 2 × 10-9 ml s-1 which is appropriate
for the measured compounds [Ezra et al. 2004; Lindinger
et al. 1998]. This method provided a means by which the
measured ion intensity at any mass can be expressed as an
equivalent estimate of concentration.
The total VOC concentration produced by the fungal
culture is taken as the difference between the fungal culture and the control measurements. This total reflects
only the contribution from those species having proton
affinities greater than that of water, which is assumed to
represent the majority of the VOCs produced.
Volatile analysis with SPME GC-MS
Solid phase microextraction (SPME) was used to identify
gaseous compounds as described previously [Griffin et al.
2010], but a brief description of the relevant details is
provided here. Throughout the growth period, 12 mL of
A. sarcoides culture was aseptically removed, sealed, centrifuged at 4,000 rpm for 4 minutes, then 10 mL of supernatant was added to a GC vial and stored at 4°C until
analysis. For analysis, the SPME fiber (divinylbenzene/
Carboxen on polydimethylsiloxane by Supelco) (Bellefonte, PA) was exposed to the vial headspace while the
sample was stirred for 45 minutes. The fiber was inserted
into the injection port of the GC at 240°C. The GC temperature was held at 40°C for 2 minutes, and then
ramped to 230°C at 5°C min-1. The mass spectrometer
was tuned to meet EPA Method 8260 BFB tuning criteria.
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The relative amount of identified compounds was estimated by comparison with a 4-bromofluorobenzene
internal standard calibration curve delivered in methanol
over the concentration range of 2 to 75 μg/mL.
The quantities of volatiles desorbed from the fiber
were calculated from the peak area of the total ion current measured by the mass spectrometer. This method
assumed that fungal VOCs and the internal standard
have the same gas-liquid partitioning, efficiency of
adsorption to the fiber, and response in the GC-MS system as that of the internal standard. As will be shown
later, all of these conditions are not met, and the quantities obtained are estimates. Nevertheless, the use of the
internal standard allowed for a relative quantitative
comparison between samples from the same culture
across a time period. Data acquisition and data processing were performed on Hewlett Packard ChemStation
software system. The identification of compounds produced by A. sarcoides was made via library comparison
using the National Institute of Standards and Technology (NIST) database, and all chemical compounds
described in this report use the NIST Chemistry WebBook terminology [Linstrom and Mallard 2011]. When
the library identified multiple compounds as matches,
careful consideration of the spectra yielded one with
much higher similarity or a determination of “unknown”
was made.
Henry’s law constants
To characterize the headspace concentration response to
a specific set of compounds at known initial concentrations using the PTR-MS and the SPME technique, a
known mixture (Table 1) was introduced into the Biostat reactor system. The reactor was sparged with humidified air at 1.5 L/min, equivalent to that employed for
the culturing conditions. Temperature (23°C) and stir
rate (250 rpm) were held constant by the Biostat control
system. A portion of the gaseous reactor effluent was
diverted to the PTR-MS to measure effluent volatile
concentrations over a time period of 48 hours to determine the Henry’s Law constants for each volatile compound. Liquid aliquots were removed from the reactor
at 0, 1, 2, 3, 6, 12, 24, and 48 hours for analysis by headspace SPME GC-MS.
The relationship governing the equilibrium concentration in the liquid in relation to the headspace is defined
by Henry’s Law:
PA =
CAhq
HA
(1)
Where r A is the partial pressure of compound A,
CAliq, is its concentration in the liquid, and HA is the
Henry’s Law constant for A. Henry’s Law constants
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Table 1 Initial concentrations and Henry’s Law constants
for compounds run as standards in the abiotic
experiment
Henry’s Law Constantsa
Standard Compound
Cob (+/-)
NISTc
Yawsd
Experimental
Acetaldehyde
2242 (415)
14
10.3
13.9
Benzaldehyde
2239 (23)
39
39.6
38.9
173
Ethanol
2822 (546)
120
121.7
Nonanal
2032 (851)
0.7
1.5
1.2
1-Octen-3-ol
1489 (653)
62e
39.4e
25.2
1-Butanol, 3-methyl
1294 (45)
73.8
77.6
a
All values in mol/kg*bar
Initial headspace concentration based on Henry’s Law constants in ppbv with
standard deviation in parenthesis
c
[Sander, R (2011)].
d
[Yaws, CL (1999)].
e
1-Octanol value, value for 1-Octen-3-ol not available
b
were evaluated from the PTR-MS measurements following the methodology of [Karl et al. (2003)] and compared with literature values (Table 1) [Linstrom and
Mallard 2011Yaws 1999]. Experimentally determined
Henry’s Law constants were evaluated from the slope of
the natural log of concentration versus time, and the
initial substrate concentration was taken as the intercept. As the concentration of each compound decreases
in the liquid, the headspace gaseous concentration in
equilibrium with the liquid can be calculated from the
first-order rate equation using the initial headspace concentration and the Henry’s Law constant.
·F
(2)
CA (t) = CAo exp
∗t
HA VRT
Where CA is the gaseous concentration of A, F is the
flow rate of gas, V is the volume of liquid containing A,
R is the ideal gas constant, and T is the temperature.
Initial concentration of compound A added to the
reactor can be measured by PTR-MS and confirmed by
calculations based on the volume of compound A
added, VA.
CAo =
VA pA
MWA HA PV
(3)
Where rA is the density of A, MWA is the molecular
weight of A, and P is the pressure of the reactor headspace. The initial concentration for each standard from
EQ. 3 is listed in Table 1.
Results
Abiotic results
Abiotic results were obtained from known concentrations
of six standard compounds in the reactor system under a
set of controlled conditions that closely mimicked the
fungal reactor system to better understand how the measurements of the PTR-MS and SPME GC/MS compare.
All of the compounds were added at a level to achieve an
initial headspace concentration of 2000 ppbv. The concentration profiles versus time for each of the standards
as measured by the PTR-MS and the SPME fiber are
shown in Figure 1. The headspace concentration of each
measured standard decreased in an exponential fashion
as predicted by Equation 2. Henry’s Law, as an equilibrium relation, dictates that the concentration of each
standard in the gas phase will decrease proportionally
with time as the liquid phase concentration decreases
due to sparging. The rate of decrease is determined by
the air sparge rate and the system mass balance and is
inversely related to the Henry’s Law constant. For example, nonanal has the smallest Henry’s Law constant
reported in Table 1 and its headspace concentration
decreased the fastest of all the analyzed compounds.
Henry’s law constants for each of the standards can be
obtained by plotting the time series data from Figure 1 as
the natural log of concentration versus time, yielding a
linear relationship (not shown) where the slope of the
line is related to the Henry’s Law constant [Karl et al.
2003]. The Henry’s Law constants calculated from the
PTR data are consistent with those reported previously in
the literature (Table 1), which indicates that the ions
monitored are free from any interferences and the
response of the PTR-MS is within the linear dynamic
range of the instrument. The SPME results agree less
favorably quantitatively and are discussed below. Qualitatively, the PTR-MS and SPME agree on the identification
of the standard compounds with the exception of ethanol
which was not detected by the SPME.
The SPME results in Figure 1 show that the divinylbenzene/Carboxen on polydimethylsiloxane fiber is inefficient at trapping acetaldehyde and ethanol. This result
is not totally unexpected, as the fiber is not specified for
the analysis of C2 compounds. It appears that the SPME
fiber did not provide results proportional to the amount
of acetaldehyde or ethanol present. Additionally, the
SPME measurements of 1-butanol, 3-methyl- had a contradictory trend to the PTR-MS measurements and did
not decrease in concentration over time. The SPME
results for the higher molecular weight components
appear to more closely follow the anticipated Henry’s
law behavior and are fairly consistent with the PTR-MS
results with correlation coefficients above 0.8.
A better means of accessing the correlation between
the PTR-MS and SPME data can be made by collecting
the concentration data at the same discrete time points.
The PTR-MS and SPME time point concentrations have
been plotted against each other in Figure 2 with the corresponding correlation factors. Note that ethanol was
not included in Figure 2, as there was no ethanol peak
Mallette et al. AMB Express 2012, 2:23
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Figure 1 Abiotic concentration profiles of standard compounds as measured by PTR-MS and SPME GC-MS. Concentration profiles of
each standard compound with abiotic measurements. PTR-MS response in parts per billion volume (ppbv) was monitored continuously over 48
hour time period. HS-SPME samples were removed at 0, 1, 2, 3, 6, 12, 24, 36 and 48 hours with correlation coefficients corresponding to six
minute PTR-MS averages.
detected in the SPME GC-MS technique. While it is
apparent in both Figures 1 and 2 that the measurement
of acetaldehyde and 1-butanol, 3-methyl- by the two
techniques do not correlate well, Figure 2 shows that
the degree of correlation for benzaldehyde and 1-octen3-ol is concentration dependent. The PTR-MS and
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r = 0.86
r = 0.84
Page 6 of 13
r = 0.86
1-butanol, 3-methylr = -0.97
r = 0.38
Figure 2 Abiotic correlation of standard compound concentrations. Correlation of relative concentration measurements for PTR-MS and the
SPME GC-MS technique for all compounds with abiotic measurements except for ethanol, which had a non-detect response by SPME GC-MS.
PTR-MS values are centered at SPME sample collection times and are six minute averages. Lines indicate linear correlation at low concentrations
with divergence at high concentrations while correlation coefficients include all data points.
Mallette et al. AMB Express 2012, 2:23
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SPME measurements appear to track one another at the
lower concentrations. At the higher concentrations, the
SPME results appear to saturate, which suggests that the
adsorption capacity of the fiber may have been
exceeded. The nonanal results appear to be similarly
affected, but the comparison is less definitive due to
rapid removal of nonanal from solution and the appearance of the non-zero intercept. The contradictory trend
for 1-butanol, 3-methyl- suggests that the adsorption
process is competitive, because the 1-butanol, 3-methylconcentration, as measured by SPME, increases as the
stronger adsorbing species decrease in concentration as
they are removed from solution. These results indicate
that the concentrations measured by the SPME technique are strongly dependent not only on the type of mixture constituents, but also on the relative concentrations
of those components. Thus even in relatively simple systems such as this, some care must be exercised in deducing quantitative information from the SPME data.
Quantification of volatile products of A. sarcoides
Applying these techniques to the biological system,
Figure 3 shows the time series of the total VOC signal
(sum of all the ions from m/z 40-205) as measured by
the PTR-MS along with a measure of A. sarcoides biological growth expressed as biomass density. The initial
variation in the VOC signal is due to inoculation of the
reactor with an active culture, and the initial peak
decays away due to the sparging process. The culture
demonstrated a lag phase until day 3 before entering an
Page 7 of 13
exponential growth phase. The total VOC concentration
increased as the fungus entered exponential phase indicating growth associated product synthesis. The flow
rate of gas supplied to the reactor was constant such
that increasing concentrations demonstrates increased
metabolic production of VOCs. The maximum ion concentration of VOCs measured was 10,000 ppbv at 284
hours (near 12 days). The cell concentration began to
stabilize on day 12 around 4.5 g/L indicating the culture’s entry into stationary phase. Once the stationary
phase was reached after day 12, the total VOC signal
stabilized, and then dropped rapidly around 380 hours.
Throughout stationary phase, the PTR-MS measured
the VOCs excluding acetaldehyde and ethanol at 2,000
ppbv. The very sharp transitions in the VOC signal are
due to variations in the sparge flow and should not be
interpreted as specific metabolic changes.
Continuous monitoring by the PTR-MS revealed that
two ions (m/z 45 (acetaldehyde) and m/z 47 (ethanol))
comprised the majority of the ion signal from the A.
sarcoides culture. Excluding m/z 45 and m/z 47, the
remaining ion intensity measured was between 1,000
and 2,000 ppbv in concentration for the majority of the
time (Figure 3). The remaining ions account for ~ 14%
of the total VOC signal. A listing of all of the ions
whose intensity exceeded 1 ppbv is tabulated in Table 2
along with their signal intensity at discrete time points.
Ions of the carbon 13 isotopes are not included, i.e. m/z
72 which is 5.5% of m/z 71. Total production of compounds in Table 2 increased to over 2,000 ppbv at
Figure 3 Total VOCs in sparged air from A. sarcoides culture as measured by PTR-MS.The total VOCs excluding the signals from ethanol
and acetaldehyde were near 2000 ppbv at their maximum. Biomass was measured by dry weight.
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Table 2 Select ion signals in the dynamic headspace of A.
sarcoides culture measured by PTR-MS
Mass (amu)a,b
Signal Intensity (ppbv) at given age in
daysc
2
5
9
11
13
15
17
33 [methanol]
1.0
3.6
6.8
9.1
11.1
10.6
12.8
41
6.3
12.7 27.1 40.8
48.8
49.7
52.2
43
36.6 87.8 198
264
251
260
269
57
11.1 44.7 129
180
187
201
234
59 [C3H6O]
17.0 71.9 198
264
251
260
269
61
7.6
23.2 69.6 87.7
60.4
54.8
52.5
69
1.7
3.5
6.6
7.3
8.6
71 [1-butanol, 3-methyl-] 12.9 24.0 76.4 99.1
98.6
111
127
73 [C4H8O]
4.4
39.8 38.6 41.3
43.8
44.1
38.2
75
0.2
0.5
1.9
2.3
1.4
1.5
1.6
87 [C5H10O]
1.0
2.2
4.0
4.9
7.4
10.0
8.0
89 [ethyl acetate]
5.3
14.1 47.9 53.4
32.0
31.0
31.6
91
0.3
0.8
2.0
2.6
2.0
2.1
2.0
93
0.2
0.2
0.9
1.1
0.6
0.8
0.9
105
0.2
0.5
1.8
2.4
2.1
2.2
2.6
107 [benzaldehyde]
0.2
6.3
7.5
6.9
3.5
3.4
3.3
111 [1-octen-3-ol]
0.1
0.5
2.5
3.4
2.6
3.8
4.4
115
0.0
0.5
1.8
1.8
0.9
0.5
0.7
117
0.2
0.4
1.5
2.1
2.0
3.0
4.2
129
0.1
0.4
0.9
1.3
1.2
1.2
1.5
143 [nonanal]
0.1
0.6
0.6
0.7
0.2
0.4
0.5
SUM
106
338
847
1086 1013 1063 1153
5.9
7.5
a
Acetaldehyde (45), ethanol (47) and minor ion signals are excluded.
b
Tentative assignments to the ion are given in brackets.
c
Signal intensity denoted as 4 hour averages associated with the data in
Table 3.
280 hours and leveled off as the fungus entered stationary phase after day 12. The majority of these compounds were composed of 4 and 5 carbon alcohols,
acetic acid or acetate esters and compounds producing
m/z 59 (acetone and/or propanal). The PTR-MS signal
from 1-butanol, 3-methyl- was over 1% of the total
VOCs. Notable compounds of fuel interest included
1-octen-3-ol, nonanal, 1-butanol, 3-methyl- and benzaldehyde (Figure 4). The PTR-MS signals of the alcohols
in Figure 4 increased steadily until near the end of the
growth cycle with a peak around 275 hours at the end
of the exponential growth phase. The benzaldehyde signal reached a maximum around 210 hours before halving just before 300 hours and remained there for the
stationary growth phase of A. sarcoides. The SPME measured concentration profiles correspond well with the
PTR-MS results for the higher molecular weight alcohols (correlation coefficients > 0.8), but not for ethanol
or benzaldehyde (correlation coefficients < 0.7). An
increase in the concentration of a compound should elicit a relative increase in the measured response of each
technique. Figure 4 and Table 2 clearly show that there
are compounds for which the SPME response to a
change in VOC concentration does not scale with the
corresponding PTR-MS signal change. The differences
in response show the confounding effects of SPME fiber
selectivity.
The total ion signal was consistent with A. sarcoides biomass concentration; meaning VOC production was closely
related with growth (Figure 3). The VOC concentration
profiles vary by compound as shown in Figure 4 with
some being associated with growth. The compounds with
the highest concentrations were acetaldehyde and ethanol.
The major ion signatures, m/z 101, m/z 119, and m/z 159
found in the [Strobel et al. (2008)] experiment on oatmeal
agar were not observed in this study. This change in product composition is most likely due to different carbon
source and experimental conditions.
Identification of volatile products of A. sarcoides
The qualitative information provided by the SPME technique identifies specific VOCs, thereby allowing better ion
assignments to be made with the PTR-MS. Most of the
compounds positively identified by the SPME technique
were also detected with PTR-MS, though the relative
amounts of the identified compounds differed significantly
between the two techniques. In some instances, the ions
detected by PTR-MS were not unique and reflect the collective signal from more than one compound; often the
case for lower mass ions. A total of twenty-seven distinct
peaks were found using SPME GC-MS, with eight being
unidentifiable (Table 3). Of the identified compounds,
there was a mixture of (in order of descending frequency)
organic acids and esters, alcohols, aldehydes, aromatics
and alkanes. Two alkanes were identified by the SPME
GC-MS (pentane and 4-methyl-heptane). The branched 8
carbon alkane, 4-methyl-heptane, was produced from Day
2 to Day 15. Physical property information for Table 3
compounds is listed in Additional file 1.
As noted in Table 3, twelve compounds identified in this
study were also detected by [Griffin et al. (2010)] on various media including cellulose. Although there were major
differences in the volatile constituents identified, this was
not surprising due to the very different experimental conditions of this study. For example, acetic acid, heptyl ester
was reported [Strobel et al. 2008] as one of most abundant
compounds, but it was not identified by SPME in these
results. However, many minor compounds identified by
[Strobel et al. (2008)] on oatmeal agar were identified in
this study despite conservative identification changes later
made [Strobel et al. 2010]. These compounds are noted in
Table 3 and include heptane, 2-methyl-; acetic acid, decyl
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Page 9 of 13
Figure 4 Measured concentrations of ethanol, 1-butanol, 3-methyl-, benzaldehyde and 1-octen-3-ol over the course of the growth
period. Response of ethanol, 1-butanol, 3-methyl-, benzaldehyde and 1-octen-3-ol over the course of the growth period as measured by PTRMS and HS-SPME GC-MS including correlation coefficient, r.
ester; ethanol; 1-propanol, 2-methyl-; phenylethyl alcohol;
1-butanol, 3-methyl-; and an isomer of 3-octen-2-ol (1octen-3-ol).
Of the compounds identified by SPME GC-MS, the
concentration profiles of several correlated with the
PTR-MS measured concentrations with correlation coefficient exceeding 0.80 (Tables 2 and 3). These compounds include acetic acid, ethyl ester (89) and the
alcohols: 1-butanol, 3-methyl- (71), 1-octen-3-ol (111)
(Figure 4). The concentration profile of 1-butanol, 3methyl- as indicated by SPME correlated well with the
PTR-MS measurements of ion 71 (Figure 4) with a correlation coefficient of 0.93, despite the negative correlation found by the abiotic results (Figures 1 and 2). A
possible explanation for this is the concentrations of
other compounds in the fungal gas phase were small
and did not preferentially displace 1-butanol, 3-methylfrom the fiber, while in the abiotic results, this was not
the case. The concentration profiles for 1-octen-3-ol
were strikingly similar despite the relatively low concentration of this product. This was also the case for butanoic acid, 3-methyl- and nonanal whose PTR-MS
measured concentrations were less than 1 ppbv. Like 1butanol, 3-methyl-, their measured concentrations steadily increased through the majority of the time points.
The variance in reported signals is seen in Tables 2 and
3 and Figure 4.
The SPME measurements of compounds that did not
correlate well with the PTR-MS signal included benzaldehyde (107) and 1-propanol, 2-methyl- (57). For benzaldehyde, the first SPME data point matches the PTR-MS
signal, but afterwards, the measured SPME concentrations
are out of step with PTR-MS concentrations (Figure 4).
There was a similar incongruity with the 1-propanol, 2methyl- measurements. The PTR-MS measurement of 1propanol, 2-methyl- is made by monitoring the m/z 57
ion. The m/z 57 ion, however, is not unique to 1-propanol,
2-methyl-, and it is probable that the lack of agreement
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Page 10 of 13
Table 3 GC-MS head-space analysis of VOCs produced by A.sarcoides culture collected with a SPME fiber
Retention
Possible Compounda
Peak Area at given age in daysb
Time (min)
(Molecular mass (Da))
2
5
9
15
17
1.34
Unknown (56)
17.8
13.0
ND
ND
ND
1.38
Pentane (72)c
ND
12.6
38.7
22.6
31.8
d
2.01
Heptane, 4-methyl- (114)
12.9
12.6
11.1
15.6
ND
2.09
Unknown (118)
10.2
9.1
7.3
8.8
6.7
3.26
Ethyl acetate (88)c
10.1
19.5
52.9
51.8
44.8
188.2
d
3.89
Ethanol (46)
79.3
121.8
157.3
223.5
6.07
Unknown (114)
ND
16.1
27.5
45.2
ND
6.40
1-Propanol, 2-methyl- (74)c,d
10.1
20.1
17.7
74.9
52.8
23.7
7.70
Unknown
11.7
11.0
7.7
11.0
8.19
1-Butanol, 3-methyl- (impure) (88)c,d
88.6
139.7
334.1
665.5
667.6
9.15
Unknown (134)
8.0
10.7
12.8
ND
ND
9.56
Unknown (159 or 88)
ND
11.7
18.7
35.8
ND
84.5
c,d(isomer)
11.65
1-Octen-3-ol (128)
ND
7.6
22.8
80.3
12.02
Acetic acid, octyl ester (172)c
15.6
7.8
3.4
2.6
4.4
12.95
Benzaldehyde (106)
5.2
302.8
211.3
679.7
453.5
13.33
Acetic acid, nonyl ester (186)c
33.9
32.8
7.5
5.5
ND
13.99
Unknown (122)
10.9
32.8
1.7
ND
ND
14.58
Acetic acid, n-decyl ester (200)c,d
19.3
9.1
ND
ND
ND
c
14.86
C15H24, sesquiterpene (204)
19.3
8.1
5.4
4.1
ND
15.95
Nonanal (142)
15.1
12.1
12.3
17.6
46.7
16.99
Benzyl alcohol (108)c,d
ND
5.1
39.3
39.1
34.9
18.37
Unknown (126)
4.4
5.1
10.0
17.0
26.8
60.8
18.71
Decanal (156)
ND
ND
ND
24.7
23.27
Butanoic acid, 3-methyl- (102)c
ND
ND
ND
8.9
20.3
24.83
Oxime-, methoxy-phenyl- (151)
ND
ND
ND
39.8
35.7
26.30
Acetic acid, 2-phenylethyl ester (164)c
ND
ND
ND
24.5
24.0
28.33
Phenylethyl alcohol (122)c,d
ND
ND
ND
37.6
36.6
a
Unknown compounds represent those whose assignment is uncertain. However, probable molecular masses are included when consistent.
b
Peak areas listed as 106; ND = signal not detected
c
identified by Griffin et al. 2010 (12)
d
identified by Strobel et al. 2008 (32, 33)
between the PTR-MS and SPME results may be due to
signal interference in the PTR-MS measurement. While
perturbations in the PTR-MS signal for benzaldehyde
could be similarly explained by signal interference, but the
abiotic results for benzaldehyde showed no interference,
eliminating this possibility.
Discussion
Taken together, the results of the PTR-MS and SPME
GC-MS techniques provide a more definitive description
of the VOCs produced by A. sarcoides for product (e.g.
biofuel) development. However, the challenges associated with the quantitative and qualitative analysis of
complex VOC mixtures in an aqueous solution are nontrivial, and neither method had the ability to both
identify and quantify the breadth of compound diversity
synthesized by this complex fungal system.
The A. sarcoides culture consistently produced numerous VOCs. There was little quantitative agreement in
VOC concentrations between the SPME technique and
the PTR-MS for the suite of compounds produced by A.
sarcoides. Both techniques have advantages for exploring
potential fuel products from fungal or other cultures.
SPME effectively identifies a range of compounds produced by the culture, and at modest concentrations, has
adequate fiber capacity for many relevant compounds.
The use of PTR-MS gives quantitative data on the evolution of volatiles permitting temporal resolution of microbial volatile production. These data can be used to help
elucidate real-time metabolism shifts of an organism for
Mallette et al. AMB Express 2012, 2:23
http://www.amb-express.com/content/2/1/23
product applications (excluding alkanes) and metabolic
modeling. The PTR-MS results may also be valuable in
guiding genetic engineering efforts to improve production rates of specific compounds..
To effectively use PTR-MS and SPME GC-MS, the
inherent differences in signal acquistion must be considered. Foremost, PTR-MS is a continuous measurement
of VOCs in the gaseous stream stripped from an aqueous culture; SPME is a measurement of the headspace
compounds that develop above a liquid aliquot after a
45 minute exposure and equilibration period. However,
SPME is not truly an equilibrium measurement, because
the concentration of the headspace is potentially being
depleted by compounds adsorbing to the fiber, lowering
their vapor pressure and thereby constantly shifting the
headspace composition (Eq. 1) [Zeng and Noblet 2002].
SPME adsorbs VOCs from the headspace then estimates
a liquid concentration based on one internal standard.
Therefore, we anticipate differences in distribution of
VOCs detected by the two methods (Figures 1 and 2).
The measurement variability that exists between the two
techniques should decrease if the gaseous effluent measured by the PTR-MS is at or near equilibrium with the
compounds in the liquid. Based on the abiotic experimental results with a known mixture of VOC standards,
equilibrium was assumed for the experimental reactor
system.
A limitation of PTR-MS is that alkanes are not ionized
by H3O+ and therefore are not detectable making it unsuitable for some biofuel applications. Additional quantitative
complications can arise due to the presence of fragment
ions (decomposition products of the protonated molecule).
While ionization via proton transfer with H3O+ is considered to be a relatively soft ionization method, many of the
components produced by A. sarcoides fragment extensively. Most notable are the alcohols that, except for
methanol, fragment by the elimination of H2O from the
protonated molecule [Buhr et al. 2002]. Thus, the alcohols
are detected as an ion corresponding to (M-OH)+. Despite
these considerations, the results clearly demonstrate the
applicability of the PTR-MS for time series resolution of
volatiles from biological cultures.
SPME analysis should reflect the chemical composition
and capacity on the SPME fiber if possible. However, not
all compounds adsorb to the fiber material with equal
affinities or at equal rates, such that the use of one internal standard is not sufficient to determine all constituent
concentrations [Pawliszyn 1997]. The preferential
adsorption by some compounds appears to be further
exacerbated if the capacity of the fiber is exceeded. The
least volatile, highest affinity compounds will be extracted
from the headspace-liquid matrix last, therefore adequate
extraction times must be observed [Pawliszyn 1999].
Therefore, drawing conclusions about the concentration
Page 11 of 13
of constituents in complex mixtures is very difficult due
to fiber selectivity and saturation characteristics. In general, the SPME fiber appeared to preferentially adsorb the
higher molecular weight compounds and therefore over
predicts their abundance (Table 3). While these ions may
have been detected by the PTR-MS, signals were low in
concentration either because their headspace concentrations were low or because of possible adsorptive losses in
the sample lines and therefore were not included in
Table 2. An outcome of this preferential adsorption was
seen in the 1-butanol, 3-methyl- data. In the fungal culture, the SPME data correlated well with the PTR-MS
signal (Figure 4), but not in the abiotic data (Figure 1)
because the culture headspace environment was dominated by lower molecular weight compounds. The negative trend seen in the abiotic results was most likely
because the higher molecular weight compounds were
preferentially adsorbed by the SPME fiber, leaving few
adsorption sites for the other compounds (Figure 1). The
extent of fiber bias for the lower volatility, higher molecular weight compounds is not easily predicted or measured for complex mixtures. However, SPME appears to
be more applicable in the fungal headspace environment
than in conditions where higher concentrations of higher
molecular weight compounds were present, such as in
the abiotic system.
The PTR-MS and SPME results demonstrated the
quantitative limits of the SPME technique even when an
internal standard was used. However, use of SPME to
estimate relative concentrations of higher molecular
weight compounds may be reasonable if application is
restricted to qualitative temporal trends and not individual quantitative time points. A possibility for improving
the SPME technique is to use replicate samples with
additional fiber types to better capture and characterize
the breadth of compounds present.
This study shows that despite their shortcomings, the
combined use of PTR-MS with a qualitative technique
such as GC-MS by headspace SPME is a valuable method
for obtaining more detailed analysis of the VOCs produced by microbial systems and can be applied to biofuel
product development. However, when quantifying compounds as produced by metabolic processes, especially
when those compounds are in a complex mixture, the
combination of multiple techniques is warranted until a
single more robust qualitative and quantitative analytical
method is developed.
Additional material
Additional file 1: Physical properties and ions of Table
3compounds. Table 3 compounds are listed with their PTR-MS ions
including fractions, Henry’s Law constant, boiling point and vapor
pressure.
Mallette et al. AMB Express 2012, 2:23
http://www.amb-express.com/content/2/1/23
Abbreviations
GC-MS: Gas Chromatography - Mass Spectrometry; NIST: National Institute of
Standards and Technology; PTR-MS: Proton Transfer Reaction - Mass
Spectrometry; SPME: Solid Phase Microextraction; VOCs: Volatile Organic
Compounds.
Acknowledgements
The authors would like to thank Elle Pankratz for her general laboratory
assistance and continuous hard work. This work was supported by the
National Science Foundation (NSF) Emerging Frontiers in Research and
Innovation (EFRI), Grant No. 0937613 and Chemical, Bioengineering,
Environmental and Transport Systems (CBET), Grant No. 0802666. Additional
funding for Natasha Mallette was provided by the NSF Integrated Graduate
and Education Research Traineeship (IGERT) Program. Manuscript preparation
was supported by the National Science Foundation (NSF) Emerging Frontiers
in Research and Innovation (EFRI), Grant No. 0937613
Author details
1
Department of Chemical and Biological Engineering, Montana State
University, Bozeman MT 59717, USA 2Center for Biofilm Engineering,
Montana State University, Bozeman MT 59717, USA 3Department of
Chemistry and Biochemistry, Montana State University, Bozeman MT 59717,
USA 4Department of Plant Sciences, Montana State University, Bozeman
Montana 59717, USA
Competing interests
The authors declare that they have no competing interests.
Received: 22 February 2012 Accepted: 5 April 2012
Published: 5 April 2012
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Cite this article as: Mallette et al.: Resolution of volatile fuel compound
profiles from Ascocoryne sarcoides: a comparison by proton transfer
reaction-mass spectrometry and solid phase microextraction gas
chromatography-mass spectrometry. AMB Express 2012 2:23.
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